Development and Preliminary Evaluation of A Soft Tissue Microtia Simulator
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Surgical simulation has been used extensively for learning microtia reconstruction and has almost exclusively involved framework creation. However, soft tissue reconstruction in microtia is equally challenging and would benefit from a simulation platform. This study aimed to describe the development and preliminary evaluation of a high-fidelity soft tissue microtia simulator. Three-dimensional modeling software, fused deposition 3-dimensional printing, adhesive techniques, silicones, and polyurethane rubbers were utilized to create a right lobular-type microtia simulator that comprises skin, subcutaneous tissue, and cartilage. Two expert microtia surgeons performed a microtia reconstruction on the simulator and evaluated its value and realism using a Likert-type questionnaire. The surgeons utilized a previously developed synthetic framework and successfully performed the critical steps of the soft tissue reconstruction, including marking, incising, dissection, removal of the cartilage remnant, drain insertion, insertion of the framework, closing of the skin, and demonstration of the soft tissue conforming over the framework using suction. A preliminary assessment of the simulator demonstrated that the simulator is anatomically accurate, realistic, and highly valuable as a training tool. A high-fidelity soft tissue microtia simulator was successfully developed and tested. The simulator provides a valuable training platform for learning a critical component of microtia reconstruction.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it